Automatic Summarization of textual content can be used to save time for end users by providing an overview of textual content (e.g., a document or an article) which can be quickly read by the user. Conventional extractive summarization techniques extract out key phrases from the input textual content, and then select a subset of these phrases to place in the summary. Summaries generated by these conventional summarization techniques, however, are often not human like. Further, such extractive methods fall short when the length of the desired summaries is small, since this calls for ways to “paraphrase” the input content succinctly to maximize the information conveyed in the summary rather than choosing the most “informative” sentences.
Recent advances in neural networks have led to the use of recurrent neural networks to generate summaries by paraphrasing documents. However, these algorithms allow for the generation of a single summary only. While a summary is supposed to contain the essential part of information in a document, what is characterized as “essential” varies from person to person. Additionally, many documents include subject matter pertaining to more than one topic of interest, such as politics and business. For some readers, business may be the primary area of interest, while some others may be more interested in politics. In this scenario, a single summary may not suit the topic-preferences of all readers.